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預測市場預言機研究

將預測市場研究為產品、智能體、儀表板及企業決策智慧的數據來源或預言機信號。適用於基於來源的市場隱含機率分析、注意事項及整合模式,不提供投資建議。當評估預測市場作為產品、智能體或儀表板的數據來源或預言機信號時使用。

預測市場預言機研究 是什麼?

這項技能旨在幫助開發者或AI智能體使用者,將預測市場作為一種數據來源、預測輸入、類似預言機的信號或決策智慧層進行研究與評估。它提供了一套結構化的工作流程,從定義決策問題、尋找相關市場事件,到評估市場隱含機率與信號品質(包括流動性、價差、市場年齡、交易者激勵、解決權威等因素)。該技能支援多種整合模式,包括研究助理、儀表板信號、智能體記憶輸入、警報觸發和情境規劃,但嚴格禁止提供投資建議或交易推薦。它適用於任何平臺(如Claude Code、Cursor、Codex等),並強調保持獨立審慎的態度,需明確指出市場操縱、流動性不足或結果模糊等風險。最終輸出應包含決策背景、市場來源、信號品質評估、對比來源、整合建議及注意事項,並以「預測市場信號是資訊輸入,而非投資建議」作為結尾。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/ECC/tree/main/skills/prediction-market-oracle-research

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

預測市場預言機研究 是做什麼的?

當預測市場被視為數據來源、預測輸入、類似預言機的信號或決策智慧層時,使用此技能。

防護欄

  • 勿將市場價格視為客觀真理。
  • 不提供投資建議或交易推薦。
  • 將交易平臺機制、流動性、激勵措施和解決規則與隱含信號分離開來。
  • 指出操縱、流動性不足、過時的市場以及結果模糊的情況。
  • 對於鏈上或執行相關系統,在授予任何寫入權限之前,請先執行 llm-trading-agent-security。

研究工作流程

  1. 定義信號所要支援的決策。
  2. 尋找相關的市場、事件、標籤和交易平臺。
  3. 記錄市場隱含機率,並附上時間戳記和來源連結。
  4. 評估信號品質:
    • 流動性
    • 價差
    • 市場年齡
    • 交易者/激勵集中度(若已知)
    • 解決權威
    • 地理或帳戶限制
  5. 與非市場來源進行比較,例如檔案、新聞、民調、研究、客戶數據或內部KPI。
  6. 建議該信號對於所述決策而言是可用的、薄弱的還是不合適。

整合模式

  • 研究助理:為人類分析師提供基於來源的背景信息。
  • 儀表板信號:市場隱含機率與內部指標並列顯示。
  • 智能體記憶輸入:帶時間戳記的信號,可供後續檢索。
  • 警報輸入:當機率、價差或流動性超過閾值時通知。
  • 情境規劃:比較多個事件結果,無需自動化交易。

輸出合約

使用:

  1. 決策背景
  2. 市場來源
  3. 信號品質
  4. 比較來源
  5. 整合建議
  6. 注意事項

結尾:

預測市場信號是資訊輸入,而非投資建議。

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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